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Redes de grafos de cápsulas para la predicción precisa e interpretable de propiedades de materiales cristalinos
Xing Wu1,2,3, Eddah K Sure4,5, Quan Qian4,5,6
1Material Genome Institute, Shanghai University, Shanghai, 200444, China. xingwu@shu.edu.cn.
Journal of cheminformatics
|December 30, 2025
Resumen
Introducimos las Redes de Grafos de Cápsulas con E(3)-Equivarianza (CGN-e3), un novedoso modelo de aprendizaje profundo para materiales cristalinos. Este marco mejora la interpretabilidad y captura las simetrías cristalinas para un descubrimiento de materiales preciso.
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